Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples.
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Authors
Masqué-Soler, Neus
Gehrung, Marcel
Kosmidou, Cassandra
Li, Xiaodun
Diwan, Izzuddin
Rafferty, Conor
Atabakhsh, Elnaz
Fitzgerald, Rebecca C
Publication Date
2022-02Journal Title
EBioMedicine
ISSN
2352-3964
Publisher
Elsevier BV
Volume
76
Number
103814
Pages
103814
Type
Article
This Version
VoR
Physical Medium
Print-Electronic
Metadata
Show full item recordCitation
Masqué-Soler, N., Gehrung, M., Kosmidou, C., Li, X., Diwan, I., Rafferty, C., Atabakhsh, E., et al. (2022). Computational pathology aids derivation of microRNA biomarker signals from Cytosponge samples.. EBioMedicine, 76 (103814), 103814. https://doi.org/10.1016/j.ebiom.2022.103814
Abstract
BACKGROUND: Non-endoscopic cell collection devices combined with biomarkers can detect Barrett's intestinal metaplasia and early oesophageal cancer. However, assays performed on multi-cellular samples lose information about the cell source of the biomarker signal. This cross-sectional study examines whether a bespoke artificial intelligence-based computational pathology tool could ascertain the cellular origin of microRNA biomarkers, to inform interpretation of the disease pathology, and confirm biomarker validity. METHODS: The microRNA expression profiles of 110 targets were assessed with a custom multiplexed panel in a cohort of 117 individuals with reflux that took a Cytosponge test. A computational pathology tool quantified the amount of columnar epithelium present in pathology slides, and results were correlated with microRNA signals. An independent cohort of 139 Cytosponges, each from an individual patient, was used to validate the findings via qPCR. FINDINGS: Seventeen microRNAs are upregulated in BE compared to healthy squamous epithelia, of which 13 remain upregulated in dysplasia. A pathway enrichment analysis confirmed association to neoplastic and cell cycle regulation processes. Ten microRNAs positively correlated with columnar epithelium content, with miRNA-192-5p and -194-5p accurately detecting the presence of gastric cells (AUC 0.97 and 0.95). In contrast, miR-196a-5p is confirmed as a specific BE marker. INTERPRETATION: Computational pathology tools aid accurate cellular attribution of molecular signals. This innovative design with multiplex microRNA coupled with artificial intelligence has led to discovery of a quality control metric suitable for large scale application of the Cytosponge. Similar approaches could aid optimal interpretation of biomarkers for clinical use. FUNDING: Funded by the NIHR Cambridge Biomedical Research Centre, the Medical Research Council, the Rosetrees and Stoneygate Trusts, and CRUK core grants.
Keywords
Artificial intelligence, Barrett's oesophagus, Computerized image analysis, Dysplasia, Oesophageal cancer, Screening
Identifiers
External DOI: https://doi.org/10.1016/j.ebiom.2022.103814
This record's URL: https://www.repository.cam.ac.uk/handle/1810/333401
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